The paper argues that a capacity for certain kinds of meta-knowledge is central to modeling consciousness, particularly the recalcitrant aspects of qualia, in computational architectures. It presents a novel objection to Frank Jackson's Knowledge Argument against physicalism, showing that the supposition of a physically omniscient person, Mary, who has not experienced seeing red, is logically inconsistent due to epistemic blindspots. Even if the argument is made consistent by assuming a more limited physical omniscience, it remains invalid because there is a physical fact (a recursive conditional epistemic blindspot) that Mary cannot know before experiencing red but can know afterward. The paper discusses implications for machine consciousness.
Medical AI systems may fail to achieve full ethical expertise because they lack machine consciousness, which underpins human medical agents' abilities such as weighing diagnostic options, planning treatments, exercising imaginative creativity, sensorimotor flexibility, and empathetic responsiveness. The authors argue that a plausible design constraint for a successful ethical machine medical or care agent is to at least model, if not reproduce, relevant aspects of consciousness and associated abilities. They examine key philosophical issues concerning machine modeling of consciousness and ethics, showing how these questions are relevant to medical machine ethics, aiming to overcome blanket skepticism about the relevance of consciousness to designing artificial ethical agents for medical contexts.
Pessimism about artificial consciousness (AC) is unfounded, resting on misunderstandings of AI and ignorance of possible roles AI might play in reproducing consciousness. Through conceptual analysis, the paper shows that common objections fail against neglected possibilities for AC: prosthetic, discriminative, practically necessary, and lagom (necessary-but-not-sufficient) AC. Three strands of the author's work—interactive empiricism, synthetic phenomenology, and ontologically conservative heterophenomenology—illustrate these distinctions and defenses.